skills.rest
victorlou
victorlouCOMMUNITY
Explore all skills by this author →
GitHub

agent-openai-memory

Store and retrieve multi-turn conversation history using OpenAI Agents SDK sessions.

4|Updated May 9, 2026
Search Tags:#sessions#agent memory#databricks#conversation state#lakebase#openai agents sdk
One-click install
npx skills add https://github.com/victorlou/housing-assistant --skill agent-openai-memory-victorlou
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-openai-memory
Source: https://github.com/victorlou/housing-assistant/tree/main/app/app-templates/.claude/skills/agent-openai-memory
Command: npx skills add https://github.com/victorlou/housing-assistant --skill agent-openai-memory-victorlou

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Stateful memory is hard to implement reliably for multi-turn agent conversations, and without it users experience repeated questions and lost preferences.

Core Features & Use Cases

  • Persist conversation history via Sessions: Store and retrieve prior messages automatically using OpenAI Agents SDK Sessions backed by Databricks Lakebase.
  • Use consistent session IDs: Link requests to the same ongoing conversation so the agent continues naturally across runs.
  • Checkpointing for user preferences: Persist conversation context for longer-term behavior such as saved user preferences and alert-related state.

Quick Start

Add the Lakebase database resource to your app and set LAKEBASE_INSTANCE_NAME so session-backed memory can persist across agent invocations.

Frequently Asked Questions about agent-openai-memory

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I persist conversation history for OpenAI agents across multiple requests?▼

You can persist conversation history for OpenAI agents across multiple requests by using OpenAI Agents SDK sessions backed by Databricks Lakebase to automatically store and retrieve prior messages.

Why does my agent lose user preferences and context in multi-turn conversations?▼

Agents lose user preferences and context without stateful memory. You need persistent conversation state via sessions to checkpoint preferences and maintain stable continuity across runs.

What do I need to configure to enable agent memory with Databricks Lakebase?▼

To enable agent memory with Databricks Lakebase, you must add the Lakebase database resource to your app and set the LAKEBASE_INSTANCE_NAME environment variable for session-backed persistence.

How do I link separate agent invocations to the same ongoing conversation?▼

You link separate agent invocations to the same ongoing conversation by using a consistent session_id with AsyncDatabricksSession so the agent continues naturally across runs.

Does OpenAI Agents SDK support checkpointing for long-term conversation state?▼

Yes, OpenAI Agents SDK supports checkpointing for long-term conversation state by using sessions to persist conversation context for saved user preferences and alert-related state.

What are the limitations of using Databricks Lakebase for agent session memory?▼

Using Databricks Lakebase for agent session memory requires a configured LAKEBASE_INSTANCE_NAME and a correct session_id with AsyncDatabricksSession, limiting usage to environments with Databricks access.

Related Skills

Explore More Software Engineering Skills→
AshDaxAshDax

agent-openai-memory

Persist conversation history for multi-turn OpenAI Agents SDK sessions using Databricks Lakebase.

Community
Advanced
RamVegirajuRamVegiraju
4

agent-openai-memory

Store and retrieve multi-turn conversation history for OpenAI agents using Lakebase-backed sessions.

Community
Advanced
omerspacomerspac

integrate-openai-agents

Integrate OpenAI Agents SDK with FastAPI for stateless chat endpoints.

Community
Advanced
databricksdatabricks
183

agent-openai-memory

Persist multi-turn conversation history using OpenAI Agents SDK Sessions and Databricks Lakebase.

Official
Intermediate